** Replication file for "Gendered Justice" The Journal of Politics ***

**Open Gendered Justice_main data file in STATA 18***
**DO NOT SAVE DATA after running the do file - CEM command changes the data***

***Match men and women as the treatment variable on similar cases***

cem Country const_crim proc_crim issue1 decade, treatment(femdef)

***Use cem_weights as iweights***

***LOGIT***
***Main model***
logit libdecis_crim laglibpct  i.femdef i.const_crim i.proc_crim i.violent i.property i.drugs i.morality  i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year)
***Main model, different syntax - same results***
logit libdecis_crim laglibpct i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year)
fitstat

***DISCRETE CHANGE****
**Constitutional - men**
quietly logit libdecis_crim laglibpct    i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year)
margins const_crim, at(femdef=(0)) post  
lincom 1.const_crim - 0.const_crim, level(95)
display ((_b[1.const_crim] - _b[0.const_crim])/_b[0.const_crim])*100
**Constitutional - women**
quietly logit libdecis_crim laglibpct    i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year)
margins const_crim, at(femdef=(1)) post
lincom 1.const_crim - 0.const_crim, level(95)
display ((_b[1.const_crim] - _b[0.const_crim])/_b[0.const_crim])*100
**Procedural - men**
quietly logit libdecis_crim laglibpct    i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year)
margins proc_crim, at(femdef=(0)) post
lincom 1.proc_crim - 0.proc_crim, level(95)
display ((_b[1.proc_crim] - _b[0.proc_crim])/_b[0.proc_crim])*100
**Procedural - women**
quietly logit libdecis_crim laglibpct    i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year) 
margins proc_crim, at(femdef=(1)) post  
lincom 1.proc_crim - 0.proc_crim, level(95)
display ((_b[1.proc_crim] - _b[0.proc_crim])/_b[0.proc_crim])*100
**Violent - men**
quietly logit libdecis_crim laglibpct    i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year)
margins violent, at(femdef=(0)) post  
lincom 1.violent - 0.violent, level(95)
display ((_b[1.violent] - _b[0.violent])/_b[0.violent])*100
**Violent - women**
quietly logit libdecis_crim laglibpct    i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year)
margins violent, at(femdef=(1)) post  
lincom 1.violent - 0.violent, level(95)
display ((_b[1.violent] - _b[0.violent])/_b[0.violent])*100
**Property - men**
quietly logit libdecis_crim laglibpct    i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year)
margins property, at(femdef=(0)) post  
lincom 1.property - 0.property, level(95)
display ((_b[1.property] - _b[0.property])/_b[0.property])*100
**Property - women**
quietly logit libdecis_crim laglibpct    i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year) 
margins property, at(femdef=(1)) post
lincom 1.property - 0.property, level(95)
display ((_b[1.property] - _b[0.property])/_b[0.property])*100
**Drugs - men**
quietly logit libdecis_crim laglibpct    i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year) 
margins drugs, at(femdef=(0)) post 
lincom 1.drugs - 0.drugs, level(95)
display ((_b[1.drugs] - _b[0.drugs])/_b[0.drugs])*100
**Drugs - women**
quietly logit libdecis_crim laglibpct    i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year)
margins drugs, at(femdef=(1)) post  
lincom 1.drugs - 0.drugs, level(95)
display ((_b[1.drugs] - _b[0.drugs])/_b[0.drugs])*100
**Morality - men**
quietly logit libdecis_crim laglibpct    i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year) 
margins morality, at(femdef=(0)) post
lincom 1.morality - 0.morality, level(95)
display ((_b[1.morality] - _b[0.morality])/_b[0.morality])*100
**Morality - women**
quietly logit libdecis_crim laglibpct    i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year) 
margins morality, at(femdef=(1)) post  
lincom 1.morality - 0.morality, level(95)
display ((_b[1.morality] - _b[0.morality])/_b[0.morality])*100

***These effects are the same as the predicted probs and discrete changes reported in the paper using different syntax***
logit libdecis_crim laglibpct i.femdef##i.const_crim i.femdef##i.proc_crim i.femdef##i.violent i.femdef##i.property i.femdef##i.drugs i.femdef##i.morality SA UK India Philip Canada [iweight=cem_weights], cluster(year)
fitstat
**Predicted probs**
margins const_crim, at(femdef= (0 1))
margins proc_crim, at(femdef=(0 1))
margins violent, at(femdef=(0 1))
margins property, at(femdef= (0 1))
margins drugs, at (femdef= (0 1))
margins morality, at (femdef = (0 1))
**Discrete change**
margins femdef, dydx(const_crim)
margins femdef, dydx(proc_crim)
margins femdef, dydx(violent)
margins femdef, dydx(property)
margins femdef, dydx(drugs)
margins femdef, dydx(morality)

**Alter country as reference in main model**
logit libdecis_crim laglibpct i.femdef i.const_crim i.proc_crim i.violent i.property i.drugs i.morality i.femdef#i.const_crim i.femdef#i.proc_crim i.femdef#i.violent i.femdef#i.property i.femdef#i.drugs i.femdef#i.morality Australia UK India Philip Canada [iweight=cem_weights], cluster(year)

logit libdecis_crim laglibpct i.femdef i.const_crim i.proc_crim i.violent i.property i.drugs i.morality i.femdef#i.const_crim i.femdef#i.proc_crim i.femdef#i.violent i.femdef#i.property i.femdef#i.drugs i.femdef#i.morality SA Australia India Philip Canada  [iweight=cem_weights], cluster(year)

logit libdecis_crim laglibpct i.femdef i.const_crim i.proc_crim i.violent i.property i.drugs i.morality i.femdef#i.const_crim i.femdef#i.proc_crim i.femdef#i.violent i.femdef#i.property i.femdef#i.drugs i.femdef#i.morality SA UK Australia Philip Canada  [iweight=cem_weights], cluster(year)

logit libdecis_crim laglibpct  i.femdef i.const_crim i.proc_crim i.violent i.property i.drugs i.morality i.femdef#i.const_crim i.femdef#i.proc_crim i.femdef#i.violent i.femdef#i.property i.femdef#i.drugs i.femdef#i.morality SA UK India Australia Canada  [iweight=cem_weights], cluster(year)

logit libdecis_crim laglibpct  i.femdef i.const_crim i.proc_crim i.violent i.property i.drugs i.morality i.femdef#i.const_crim i.femdef#i.proc_crim i.femdef#i.violent i.femdef#i.property i.femdef#i.drugs i.femdef#i.morality SA UK India Philip Australia [iweight=cem_weights], cluster(year)


































